def score(
result_folder,
epoch=None,
use_cpu=False):
"""Test a network on the dataset."""
if use_cpu:
bnet.set_mode_cpu()
else:
bnet.set_mode_gpu()
_LOGGER.info("Loading data...")
tr_data, _ = training_data()
te_data, te_labels = test_data()
from data import _MEAN
_LOGGER.info("Loading network...")
# Load the model.
model, _, _, _ = _model(result_folder,
tr_data.shape[0],
epoch=epoch,
no_solver=True)
_LOGGER.info("Predicting...")
results = model.predict(te_data,
test_callbacks=[
RandCropMonitor('data', _MEAN),
mnt.ProgressIndicator()
],
out_blob_names=['score'])
_LOGGER.info("Accuracy: %f.",
accuracy_score(te_labels,
np.argmax(np.array(results), axis=1)))
def test_image(
result_folder,
epoch=None,
image_idx=0,
use_cpu=False):
"""Test a network on one test image."""
if use_cpu:
bnet.set_mode_cpu()
else:
bnet.set_mode_gpu()
_LOGGER.info("Loading data...")
tr_data, _ = training_data()
te_data, _ = test_data()
from data import _MEAN
_LOGGER.info("Loading network...")
# Load the model for training.
model, _, _, _ = _model(result_folder,
tr_data.shape[0],
epoch=epoch)
_LOGGER.info("Predicting...")
results = model.predict(te_data[:image_idx + 1],
test_callbacks=[
RandCropMonitor('data', _MEAN),
mnt.ProgressIndicator()
],
out_blob_names=['score'])
_LOGGER.info("Prediction for image %d: %s.",
image_idx, str(results[image_idx]))
def test_image(result_folder, epoch=None, image_idx=0, use_cpu=False):
"""Test a network on one test image."""
if use_cpu:
bnet.set_mode_cpu()
else:
bnet.set_mode_gpu()
_LOGGER.info("Loading data...")
tr_data, _ = training_data()
te_data, _ = test_data()
_LOGGER.info("Loading network...")
# Load the model for training.
model, _, _, _ = _model(result_folder, tr_data.shape[0], epoch=epoch)
_LOGGER.info("Predicting...")
results = model.predict(te_data, test_callbacks=[mnt.ProgressIndicator()])
_LOGGER.info("Prediction for image %d: %s.", image_idx,
str(results[image_idx]))
def test_image(
result_folder,
epoch=None,
image_idx=0,
use_cpu=False):
"""Test a network on one test image."""
if use_cpu:
bnet.set_mode_cpu()
else:
bnet.set_mode_gpu()
_LOGGER.info("Loading data...")
tr_data, _ = training_data()
te_data, _ = test_data()
_LOGGER.info("Loading network...")
# Load the model for training.
model, _, _, _ = _model(result_folder,
tr_data.shape[0],
epoch=epoch)
_LOGGER.info("Predicting...")
results = model.predict(te_data,
test_callbacks=[mnt.ProgressIndicator()])
_LOGGER.info("Prediction for image %d: %s.",
image_idx, str(results[image_idx]))
